Literature Reviews: Types of Clinical Study Designs
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Types of Study Designs
Meta-Analysis A way of combining data from many different research studies. A meta-analysis is a statistical process that combines the findings from individual studies. Example : Anxiety outcomes after physical activity interventions: meta-analysis findings . Conn V. Nurs Res . 2010 May-Jun;59(3):224-31.
Systematic Review A summary of the clinical literature. A systematic review is a critical assessment and evaluation of all research studies that address a particular clinical issue. The researchers use an organized method of locating, assembling, and evaluating a body of literature on a particular topic using a set of specific criteria. A systematic review typically includes a description of the findings of the collection of research studies. The systematic review may also include a quantitative pooling of data, called a meta-analysis. Example : Complementary and alternative medicine use among women with breast cancer: a systematic review. Wanchai A, Armer JM, Stewart BR. Clin J Oncol Nurs . 2010 Aug;14(4):E45-55.
Randomized Controlled Trial A controlled clinical trial that randomly (by chance) assigns participants to two or more groups. There are various methods to randomize study participants to their groups. Example : Meditation or exercise for preventing acute respiratory infection: a randomized controlled trial . Barrett B, et al. Ann Fam Med . 2012 Jul-Aug;10(4):337-46.
Cohort Study (Prospective Observational Study) A clinical research study in which people who presently have a certain condition or receive a particular treatment are followed over time and compared with another group of people who are not affected by the condition. Example : Smokeless tobacco cessation in South Asian communities: a multi-centre prospective cohort study . Croucher R, et al. Addiction. 2012 Dec;107 Suppl 2:45-52.
Case-control Study Case-control studies begin with the outcomes and do not follow people over time. Researchers choose people with a particular result (the cases) and interview the groups or check their records to ascertain what different experiences they had. They compare the odds of having an experience with the outcome to the odds of having an experience without the outcome. Example : Non-use of bicycle helmets and risk of fatal head injury: a proportional mortality, case-control study . Persaud N, et al. CMAJ . 2012 Nov 20;184(17):E921-3.
Cross-sectional study The observation of a defined population at a single point in time or time interval. Exposure and outcome are determined simultaneously. Example : Fasting might not be necessary before lipid screening: a nationally representative cross-sectional study . Steiner MJ, et al. Pediatrics . 2011 Sep;128(3):463-70.
Case Reports and Series A report on a series of patients with an outcome of interest. No control group is involved. Example : Students mentoring students in a service-learning clinical supervision experience: an educational case report . Lattanzi JB, et al. Phys Ther . 2011 Oct;91(10):1513-24.
Ideas, Editorials, Opinions Put forth by experts in the field. Example : Health and health care for the 21st century: for all the people . Koop CE. Am J Public Health . 2006 Dec;96(12):2090-2.
Animal Research Studies Studies conducted using animal subjects. Example : Intranasal leptin reduces appetite and induces weight loss in rats with diet-induced obesity (DIO) . Schulz C, Paulus K, Jöhren O, Lehnert H. Endocrinology . 2012 Jan;153(1):143-53.
Test-tube Lab Research "Test tube" experiments conducted in a controlled laboratory setting.
Adapted from Study Designs. In NICHSR Introduction to Health Services Research: a Self-Study Course. http://www.nlm.nih.gov/nichsr/ihcm/06studies/studies03.html and Glossary of EBM Terms. http://www.cebm.utoronto.ca/glossary/index.htm#top
Study Design Terminology
Bias - Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions.
Case Control Studies - Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.
Causality - The relating of causes to the effects they produce. Causes are termed necessary when they must always precede an effect and sufficient when they initiate or produce an effect. Any of several factors may be associated with the potential disease causation or outcome, including predisposing factors, enabling factors, precipitating factors, reinforcing factors, and risk factors.
Control Groups - Groups that serve as a standard for comparison in experimental studies. They are similar in relevant characteristics to the experimental group but do not receive the experimental intervention.
Controlled Clinical Trials - Clinical trials involving one or more test treatments, at least one control treatment, specified outcome measures for evaluating the studied intervention, and a bias-free method for assigning patients to the test treatment. The treatment may be drugs, devices, or procedures studied for diagnostic, therapeutic, or prophylactic effectiveness. Control measures include placebos, active medicines, no-treatment, dosage forms and regimens, historical comparisons, etc. When randomization using mathematical techniques, such as the use of a random numbers table, is employed to assign patients to test or control treatments, the trials are characterized as Randomized Controlled Trials.
Cost-Benefit Analysis - A method of comparing the cost of a program with its expected benefits in dollars (or other currency). The benefit-to-cost ratio is a measure of total return expected per unit of money spent. This analysis generally excludes consideration of factors that are not measured ultimately in economic terms. Cost effectiveness compares alternative ways to achieve a specific set of results.
Cross-Over Studies - Studies comparing two or more treatments or interventions in which the subjects or patients, upon completion of the course of one treatment, are switched to another. In the case of two treatments, A and B, half the subjects are randomly allocated to receive these in the order A, B and half to receive them in the order B, A. A criticism of this design is that effects of the first treatment may carry over into the period when the second is given.
Cross-Sectional Studies - Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.
Double-Blind Method - A method of studying a drug or procedure in which both the subjects and investigators are kept unaware of who is actually getting which specific treatment.
Empirical Research - The study, based on direct observation, use of statistical records, interviews, or experimental methods, of actual practices or the actual impact of practices or policies.
Evaluation Studies - Works consisting of studies determining the effectiveness or utility of processes, personnel, and equipment.
Genome-Wide Association Study - An analysis comparing the allele frequencies of all available (or a whole genome representative set of) polymorphic markers in unrelated patients with a specific symptom or disease condition, and those of healthy controls to identify markers associated with a specific disease or condition.
Intention to Treat Analysis - Strategy for the analysis of Randomized Controlled Trial that compares patients in the groups to which they were originally randomly assigned.
Logistic Models - Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.
Longitudinal Studies - Studies in which variables relating to an individual or group of individuals are assessed over a period of time.
Lost to Follow-Up - Study subjects in cohort studies whose outcomes are unknown e.g., because they could not or did not wish to attend follow-up visits.
Matched-Pair Analysis - A type of analysis in which subjects in a study group and a comparison group are made comparable with respect to extraneous factors by individually pairing study subjects with the comparison group subjects (e.g., age-matched controls).
Meta-Analysis - Works consisting of studies using a quantitative method of combining the results of independent studies (usually drawn from the published literature) and synthesizing summaries and conclusions which may be used to evaluate therapeutic effectiveness, plan new studies, etc. It is often an overview of clinical trials. It is usually called a meta-analysis by the author or sponsoring body and should be differentiated from reviews of literature.
Numbers Needed To Treat - Number of patients who need to be treated in order to prevent one additional bad outcome. It is the inverse of Absolute Risk Reduction.
Odds Ratio - The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.
Patient Selection - Criteria and standards used for the determination of the appropriateness of the inclusion of patients with specific conditions in proposed treatment plans and the criteria used for the inclusion of subjects in various clinical trials and other research protocols.
Predictive Value of Tests - In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.
Prospective Studies - Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.
Qualitative Studies - Research that derives data from observation, interviews, or verbal interactions and focuses on the meanings and interpretations of the participants.
Quantitative Studies - Quantitative research is research that uses numerical analysis.
Random Allocation - A process involving chance used in therapeutic trials or other research endeavor for allocating experimental subjects, human or animal, between treatment and control groups, or among treatment groups. It may also apply to experiments on inanimate objects.
Randomized Controlled Trial - Clinical trials that involve at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table.
Reproducibility of Results - The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
Retrospective Studies - Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.
Sample Size - The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups.
Sensitivity and Specificity - Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition.
Single-Blind Method - A method in which either the observer(s) or the subject(s) is kept ignorant of the group to which the subjects are assigned.
Time Factors - Elements of limited time intervals, contributing to particular results or situations.
Source: NLM MeSH Database
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An introduction to different types of study design
Posted on 6th April 2021 by Hadi Abbas
Study designs are the set of methods and procedures used to collect and analyze data in a study.
Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.
- Describes specific characteristics in a population of interest
- The most common forms are case reports and case series
- In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
- In a case series, several patients with similar experiences are grouped.
Analytical studies are of 2 types: observational and experimental.
Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes. On the other hand, in experimental studies, we conduct experiments and interventions.
Observational studies include many subtypes. Below, I will discuss the most common designs.
- This design is transverse where we take a specific sample at a specific time without any follow-up
- It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
- This design is easy to conduct
- For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.
- We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
- It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
- Prospective : we follow the individuals in the future to know who will develop the disease
- Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
- This design is the strongest among the observational studies
- For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.
- We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
- This design is always retrospective
- We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
- Relatively easy to conduct
- For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.
- Also known as interventional studies
- Can involve animals and humans
- Pre-clinical trials involve animals
- Clinical trials are experimental studies involving humans
- In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:
I: We aim to assess the safety of the drug ( is it safe ? )
II: We aim to assess the efficacy of the drug ( does it work ? )
III: We want to know if this drug is better than the old treatment ( is it better ? )
IV: We follow-up to detect long-term side effects ( can it stay in the market ? )
- In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.
Finally, the figure below will help you with your understanding of different types of study designs.
You may also be interested in the following blogs for further reading:
An introduction to randomized controlled trials
Case-control and cohort studies: a brief overview
Cohort studies: prospective and retrospective designs
Prevalence vs Incidence: what is the difference?
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No Comments on An introduction to different types of study design
Very informative and easy understandable
You are my kind of doctor. Do not lose sight of your objective.
Wow very erll explained and easy to understand
I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you
well understood,thank you so much
Simply explained. Thank You.
Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before
That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.
it is very informative and useful.
thank you statistician
Fabulous to hear, thank you John.
Thanks for this information
Thanks so much for this information….I have clearly known the types of study design Thanks
That’s so good to hear, Mirembe, thank you for letting the author know.
Very helpful article!! U have simplified everything for easy understanding
I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.
That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!
Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you
Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.
However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma
Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)
You have give a good explaination of what am looking for. However, references am not sure of where to get them from.
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Cluster Randomized Trials: Concepts
This blog summarizes the concepts of cluster randomization, and the logistical and statistical considerations while designing a cluster randomized controlled trial.
Expertise-based Randomized Controlled Trials
This blog summarizes the concepts of Expertise-based randomized controlled trials with a focus on the advantages and challenges associated with this type of study.
A well-designed cohort study can provide powerful results. This blog introduces prospective and retrospective cohort studies, discussing the advantages, disadvantages and use of these type of study designs.
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Carey TS, Sanders GD, Viswanathan M, et al. Framework for Considering Study Designs for Future Research Needs [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2012 Mar. (Methods Future Research Needs Reports, No. 8.)
Framework for Considering Study Designs for Future Research Needs [Internet].
Examples of study design considerations.
The examples that follow illustrate the team’s thinking about the content and format of demonstrating study design considerations within FRN reports. The first three are examples of a single FRN; the fourth example is a discussion of sample size issues. The examples are derived from the first series of EPC FRN reports; the needs have been reformatted to reflect the recommended presentation described in this paper. A tabular format is considered most succinct ( Example 2 ; see Table 2 ). As discussed above, the descriptors of the study design considerations should be brief. Only one to three (rarely more) of the most feasible study designs should be presented; we believe it is redundant and potentially confusing to present iterations of why, for example, a cross-sectional study design is inappropriate as a means of filling an evidence gap related to treatment effectiveness. For some gaps, only an RCT might suffice and, therefore, only one study design presentation is appropriate. As discussed previously, the authors of the FRN report should encourage creativity and emphasize that these considerations are meant to be illustrative, not prescriptive. Further, advances in analytic methods may enable alternative study designs not anticipated by the FRN team. Additionally, the FRN project team should consider and discuss the lessons learned from studies included in the CER. A discussion of methodological weaknesses that limit the strength of available evidence could be used to support a suggestion that would prevent repeating previous mistakes. Methodological evidence gaps, if apparent, should also be addressed in the study design considerations.
Comparison of study designs.
Example 1. Narrative/Bulleted Text
Content area: Fixation of fractured hip: “Do certain procedures (e.g., internal fixation) work better than others for frail elder patients?” 8
- Advantages of study design for producing a valid result : A well-done RCT will produce the most convincing results and, if inclusion/exclusion criteria and setting are realistic, should be fairly generalizable.
- Resource use, size, and duration : An RCT has to be large, because the question compares active treatments and the effect size may be small and easily swamped by other causes of morbidity and mortality in this population. Duration depends on whether the trial focuses on peri-procedural complications and short-term outcomes or on the longer-term durability of different treatment options. In either case, the resource requirements will be large or very large, given that the effect size between the treatments might be modest.
- Ethical issues : As long as equipoise exists among the treatment options, ethical issues regarding enrollment should not be present. However, if the study includes patients with dementia, consent issues may occur.
- Availability of data or ability to recruit : Recruitment may be slow, because this is a subpopulation of the population of hip fracture patients, and it may be difficult to reach large numbers.
Prospective Cohort Study
- Advantages of study design for producing a valid result : Although concern for selection bias and unmeasured confounders will always exist, the prospective design allows data for the most relevant known confounders to be collected and controlled for. Therefore, while the results will not be as definitive as an RCT, they could be informative.
- Resource use, size, and duration : This type of study still requires a large size because of potentially small treatment effects, but it would likely be less expensive than an RCT.
- Ethical, legal, and social issues : The main ethical issue is consent in the case of patients with dementia; however, because choice of treatment is not involved, it may be of less concern.
- Availability of data or ability to recruit : Recruiting patients for this design should be easier than for an RCT.
Retrospective Cohort Study
- Advantages of study design for producing a valid result : Significant risk of selection bias exists, and there is less ability to control for confounders than in a prospective cohort study because key variables may not be collected. However, this design could be sufficient for hypothesis generation that could then be used to design a more focused RCT.
- Resource use, size, and duration : A retrospective cohort study design has the potential to be considerably faster and less expensive than either an RCT or a prospective cohort study.
- Ethical, legal, and social issues : Confidentiality and Health Insurance Portability and Account (HIPAA) † issues may arise when diverse databases are linked without specific patient consent.
- Availability of data or ability to recruit : Recruiting is very feasible; the main concern is selection bias, depending on the source of the secondary data, and missing variables. Negotiations with the holders of the secondary data may take significant time.
Example 2. Table
Content area: Elective Cesarean section compared with planned vaginal delivery in healthy women. “What is the comparative effectiveness of planned Cesarean delivery versus planned vaginal delivery on maternal and neonatal outcomes?” 9
Example 3. Process or Methods Considerations
Content area: Treatment of prostate carcinoma. “Facilitate future research on potential biomarkers to identity patients whose disease is likely to be aggressive.” 10
Context: Although many efforts have been made to predict which patients with localized prostate cancer have aggressive disease, existing tools are inadequate to predict which patient to treat with any high degree of accuracy. With the emergence of biomarkers in other diseases, such as breast cancer, that have both prognostic and predictive power, the search continues to identify biomarkers that can predict which patients with prostate cancer face a poorer prognosis and may benefit to a greater degree from immediate treatment. Although a number of biomarkers have been explored to date with limited success, biomarkers continue to have a potentially important role.
Proposed research design: Establish biospecimen repositories with clinical data on diagnosis, treatment, and follow-up.
Study design considerations:
- Advantages of study design for producing a valid result : Biospecimen repositories create the resources needed to test the use of novel biomarkers in the future, while providing long-term data on outcomes that would take a long time to collect. Such repositories are being established for other studies, such as the ProtecT trial in the United Kingdom. In addition, given the differences in treatment regimens, populations, and possibly outcomes across studies, biospecimens from different trials might help address alternative hypotheses. The National Cancer Institute is establishing methods for each step of the process for creating and maintaining biospecimen repositories.
- Resource use, size, and duration : Although expensive to create and maintain, additional repositories will allow more biomarker testing, particularly because tissue specimens are finite. The administrative complexity of tracking specimens and their use is substantial, and ongoing infrastructural funding is essential.
- Ability to recruit : At the time of biopsy or surgery, patients could be consented for participation. Given that tissue is obtained as part of the procedure, this should be straightforward.
- Ethical, legal, and social issues : Biorepositories require extensive documentation of their policies regarding tracking and use of specimens. The proposed revisions to the Federal Department of Health and Human Services (HHS) Common Rule may partially address these issues. ‡ Significant planning will be needed.
Example 4. Sample Size Calculations To Inform Feasibility of Future RCTs
Content area: Coronary artery stenting compared with coronary artery bypass surgery.
Percutaneous coronary interventions (PCI) with or without stents and coronary artery bypass graft surgery (CABG) are clinically relevant treatment options for many patients with coronary artery disease (CAD). In assessing this topic, it was deemed that an important gap pertained to the comparative effectiveness and safety of the interventions in the elderly (aged 75 or older). 11
But exactly what merits further study? A focused value-of-information analysis helped clarify the group of parameters that would most inform the decision of choosing between PCI and CABG in the elderly. The analysis suggested that the relative safety of the interventions (i.e., relative effects on post-intervention complications) was more important than, for example, the absolute frequency of adverse events in the postintervention. As per descriptions above, a possible design to address relative effects of treatments is an RCT.
Is it realistic to consider a new RCT to compare PCI versus CABG? One can perform high-level sample size calculations. The biggest trials in the field enrolled approximately 2,500 patients, which serves as an indication of a large feasible RCT. Figure 2 shows power attained over a range of sample sizes for various control rate values over a mean followup of 3 or 5 years (see legend for details). Power increases with sample size, with control rate, and with length of followup. Over 5 years of followup, a study of approximately 2,500 patients would attain 80 to 90 percent power to find a relative effect of 0.80 only if it chooses an outcome that has at least a 30 percent control rate. This means a composite outcome . To get average followup duration of approximately 5 years, a trial would have to go on for 6 to 8 years at least (see legend).
Power calculations for superiority RCTs for various 5-year primary event rates in the comparator arm. Plotted are power calculations for six different 5-year primary event rates in the comparator arm (5%, 10%, 15%, 20%, 30%, and 40%, as shown next to (more...)
Therefore, de novo RCTs are feasible but would likely require resources comparable to recent large RCTs. The above calculations are generic and thus apply to any subset of patients with heart disease. For example, in middle-aged patients with two vessel disease, one would have to define a composite outcome of death or myocardial infarction or other cardiac events to attain a high event control rate and, thus, high power to detect a significant difference. By contrast, in the subpopulation of elderly patients (e.g., older than 75 years), where mortality rates can be high enough, one may be able to attain high statistical power for the outcome of death alone.
The Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy and Security Rules
The Common Rule is located at 45 CFR part 46, subpart A, and described at: http://www .hhs.gov/ohrp /humansubjects/commonrule/index.html .
- Cite this Page Carey TS, Sanders GD, Viswanathan M, et al. Framework for Considering Study Designs for Future Research Needs [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2012 Mar. (Methods Future Research Needs Reports, No. 8.) Examples of Study Design Considerations.
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This short article gives a brief guide to the different study types and a comparison of the advantages and disadvantages.
See also Levels of Evidence
These study designs all have similar components (as we’d expect from the PICO):
- A defined population (P) from which groups of subjects are studied
- Outcomes (O) that are measured
And for experimental and analytic observational studies:
- Interventions (I) or exposures (E) that are applied to different groups of subjects
Overview of the design tree
Figure 1 shows the tree of possible designs, branching into subgroups of study designs by whether the studies are descriptive or analytic and by whether the analytic studies are experimental or observational. The list is not completely exhaustive but covers most basics designs.
Figure: Tree of different types of studies (Q1, 2, and 3 refer to the three questions below)
> Download a PDF by Jeremy Howick about study designs
Our first distinction is whether the study is analytic or non-analytic. A non-analytic or descriptive study does not try to quantify the relationship but tries to give us a picture of what is happening in a population, e.g., the prevalence, incidence, or experience of a group. Descriptive studies include case reports, case-series, qualitative studies and surveys (cross-sectional) studies, which measure the frequency of several factors, and hence the size of the problem. They may sometimes also include analytic work (comparing factors “” see below).
An analytic study attempts to quantify the relationship between two factors, that is, the effect of an intervention (I) or exposure (E) on an outcome (O). To quantify the effect we will need to know the rate of outcomes in a comparison (C) group as well as the intervention or exposed group. Whether the researcher actively changes a factor or imposes uses an intervention determines whether the study is considered to be observational (passive involvement of researcher), or experimental (active involvement of researcher).
In experimental studies, the researcher manipulates the exposure, that is he or she allocates subjects to the intervention or exposure group. Experimental studies, or randomised controlled trials (RCTs), are similar to experiments in other areas of science. That is, subjects are allocated to two or more groups to receive an intervention or exposure and then followed up under carefully controlled conditions. Such studies controlled trials, particularly if randomised and blinded, have the potential to control for most of the biases that can occur in scientific studies but whether this actually occurs depends on the quality of the study design and implementation.
In analytic observational studies, the researcher simply measures the exposure or treatments of the groups. Analytical observational studies include case””control studies, cohort studies and some population (cross-sectional) studies. These studies all include matched groups of subjects and assess of associations between exposures and outcomes.
Observational studies investigate and record exposures (such as interventions or risk factors) and observe outcomes (such as disease) as they occur. Such studies may be purely descriptive or more analytical.
We should finally note that studies can incorporate several design elements. For example, a the control arm of a randomised trial may also be used as a cohort study; and the baseline measures of a cohort study may be used as a cross-sectional study.
Spotting the study design
The type of study can generally be worked at by looking at three issues (as per the Tree of design in Figure 1):
Q1. What was the aim of the study?
- To simply describe a population (PO questions) descriptive
- To quantify the relationship between factors (PICO questions) analytic.
Q2. If analytic, was the intervention randomly allocated?
- No? Observational study
For observational study the main types will then depend on the timing of the measurement of outcome, so our third question is:
Q3. When were the outcomes determined?
- Some time after the exposure or intervention? cohort study (‘prospective study’)
- At the same time as the exposure or intervention? cross sectional study or survey
- Before the exposure was determined? case-control study (‘retrospective study’ based on recall of the exposure)
Advantages and Disadvantages of the Designs
Randomised Controlled Trial
An experimental comparison study in which participants are allocated to treatment/intervention or control/placebo groups using a random mechanism (see randomisation). Best for study the effect of an intervention.
- unbiased distribution of confounders;
- blinding more likely;
- randomisation facilitates statistical analysis.
- expensive: time and money;
- volunteer bias;
- ethically problematic at times.
A controlled trial where each study participant has both therapies, e.g, is randomised to treatment A first, at the crossover point they then start treatment B. Only relevant if the outcome is reversible with time, e.g, symptoms.
- all subjects serve as own controls and error variance is reduced thus reducing sample size needed;
- all subjects receive treatment (at least some of the time);
- statistical tests assuming randomisation can be used;
- blinding can be maintained.
- all subjects receive placebo or alternative treatment at some point;
- washout period lengthy or unknown;
- cannot be used for treatments with permanent effects
Data are obtained from groups who have been exposed, or not exposed, to the new technology or factor of interest (eg from databases). No allocation of exposure is made by the researcher. Best for study the effect of predictive risk factors on an outcome.
- ethically safe;
- subjects can be matched;
- can establish timing and directionality of events;
- eligibility criteria and outcome assessments can be standardised;
- administratively easier and cheaper than RCT.
- controls may be difficult to identify;
- exposure may be linked to a hidden confounder;
- blinding is difficult;
- randomisation not present;
- for rare disease, large sample sizes or long follow-up necessary.
Patients with a certain outcome or disease and an appropriate group of controls without the outcome or disease are selected (usually with careful consideration of appropriate choice of controls, matching, etc) and then information is obtained on whether the subjects have been exposed to the factor under investigation.
- quick and cheap;
- only feasible method for very rare disorders or those with long lag between exposure and outcome;
- fewer subjects needed than cross-sectional studies.
- reliance on recall or records to determine exposure status;
- selection of control groups is difficult;
- potential bias: recall, selection.
A study that examines the relationship between diseases (or other health-related characteristics) and other variables of interest as they exist in a defined population at one particular time (ie exposure and outcomes are both measured at the same time). Best for quantifying the prevalence of a disease or risk factor, and for quantifying the accuracy of a diagnostic test.
- cheap and simple;
- ethically safe.
- establishes association at most, not causality;
- recall bias susceptibility;
- confounders may be unequally distributed;
- Neyman bias;
- group sizes may be unequal.
Types of Study Design
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Study designs are frameworks used in medical research to gather data and explore a specific research question .
Choosing an appropriate study design is one of many essential considerations before conducting research to minimise bias and yield valid results .
This guide provides a summary of study designs commonly used in medical research, their characteristics, advantages and disadvantages.
Case-report and case-series
A case report is a detailed description of a patient’s medical history, diagnosis, treatment, and outcome. A case report typically documents unusual or rare cases or reports new or unexpected clinical findings .
A case series is a similar study that involves a group of patients sharing a similar disease or condition. A case series involves a comprehensive review of medical records for each patient to identify common features or disease patterns. Case series help better understand a disease’s presentation, diagnosis, and treatment.
While a case report focuses on a single patient, a case series involves a group of patients to provide a broader perspective on a specific disease. Both case reports and case series are important tools for understanding rare or unusual diseases .
Advantages of case series and case reports include:
- Able to describe rare or poorly understood conditions or diseases
- Helpful in generating hypotheses and identifying patterns or trends in patient populations
- Can be conducted relatively quickly and at a lower cost compared to other research designs
Disadvantages of case series and case reports include:
- Prone to selection bias , meaning that the patients included in the series may not be representative of the general population
- Lack a control group, which makes it difficult to conclude the effectiveness of different treatments or interventions
- They are descriptive and cannot establish causality or control for confounding factors
A cross-sectional study aims to measure the prevalence or frequency of a disease in a population at a specific point in time. In other words, it provides a “ snapshot ” of the population at a single moment in time.
Cross-sectional studies are unique from other study designs in that they collect data on the exposure and the outcome of interest from a sample of individuals in the population. This type of data is used to investigate the distribution of health-related conditions and behaviours in different populations, which is especially useful for guiding the development of public health interventions .
Example of a cross-sectional study
A cross-sectional study might investigate the prevalence of hypertension (the outcome) in a sample of adults in a particular region. The researchers would measure blood pressure levels in each participant and gather information on other factors that could influence blood pressure, such as age, sex, weight, and lifestyle habits (exposure).
Advantages of cross-sectional studies include:
- Relatively quick and inexpensive to conduct compared to other study designs, such as cohort or case-control studies
- They can provide a snapshot of the prevalence and distribution of a particular health condition in a population
- They can help to identify patterns and associations between exposure and outcome variables, which can be used to generate hypotheses for further research
Disadvantages of cross-sectional studies include:
- They cannot establish causality , as they do not follow participants over time and cannot determine the temporal sequence between exposure and outcome
- Prone to selection bias , as the sample may not represent the entire population being studied
- They cannot account for confounding variables , which may affect the relationship between the exposure and outcome of interest
A case-control study compares people who have developed a disease of interest ( cases ) with people who have not developed the disease ( controls ) to identify potential risk factors associated with the disease.
Once cases and controls have been identified, researchers then collect information about related risk factors , such as age, sex, lifestyle factors, or environmental exposures, from individuals. By comparing the prevalence of risk factors between the cases and the controls, researchers can determine the association between the risk factors and the disease.
Example of a case-control study
A case-control study design might involve comparing a group of individuals with lung cancer (cases) to a group of individuals without lung cancer (controls) to assess the association between smoking (risk factor) and the development of lung cancer.
Advantages of case-control studies include:
- Useful for studying rare diseases , as they allow researchers to selectively recruit cases with the disease of interest
- Useful for investigating potential risk factors for a disease, as the researchers can collect data on many different factors from both cases and controls
- Can be helpful in situations where it is not ethical or practical to manipulate exposure levels or randomise study participants
Disadvantages of case-control studies include:
- Prone to selection bias , as the controls may not be representative of the general population or may have different underlying risk factors than the cases
- Cannot establish causality , as they can only identify associations between factors and disease
- May be limited by the availability of suitable controls , as finding appropriate controls who have similar characteristics to the cases can be challenging
A cohort study follows a group of individuals (a cohort) over time to investigate the relationship between an exposure or risk factor and a particular outcome or health condition. Cohort studies can be further classified into prospective or retrospective cohort studies.
Prospective cohort study
A prospective cohort study is a study in which the researchers select a group of individuals who do not have a particular disease or outcome of interest at the start of the study.
They then follow this cohort over time to track the number of patients who develop the outcome . Before the start of the study, information on exposure(s) of interest may also be collected.
Example of a prospective cohort study
A prospective cohort study might follow a group of individuals who have never smoked and measure their exposure to tobacco smoke over time to investigate the relationship between smoking and lung cancer .
Retrospective cohort study
In contrast, a retrospective cohort study is a study in which the researchers select a group of individuals who have already been exposed to something (e.g. smoking) and look back in time (for example, through patient charts) to see if they developed the outcome (e.g. lung cancer ).
The key difference in retrospective cohort studies is that data on exposure and outcome are collected after the outcome has occurred.
Example of a retrospective cohort study
A retrospective cohort study might look at the medical records of smokers and see if they developed a particular adverse event such as lung cancer.
Advantages of cohort studies include:
- Generally considered to be the most appropriate study design for investigating the temporal relationship between exposure and outcome
- Can provide estimates of incidence and relative risk , which are useful for quantifying the strength of the association between exposure and outcome
- Can be used to investigate multiple outcomes or endpoints associated with a particular exposure, which can help to identify unexpected effects or outcomes
Disadvantages of cohort studies include:
- Can be expensive and time-consuming to conduct, particularly for long-term follow-up
- May suffer from selection bias , as the sample may not be representative of the entire population being studied
- May suffer from attrition bias , as participants may drop out or be lost to follow-up over time
A meta-analysis is a type of study that involves extracting outcome data from all relevant studies in the literature and combining the results of multiple studies to produce an overall estimate of the effect size of an intervention or exposure.
Meta-analysis is often conducted alongside a systematic review and can be considered a study of studies . By doing this, researchers provide a more comprehensive and reliable estimate of the overall effect size and their confidence interval (a measure of precision).
Meta-analyses can be conducted for a wide range of research questions , including evaluating the effectiveness of medical interventions, identifying risk factors for disease, or assessing the accuracy of diagnostic tests. They are particularly useful when the results of individual studies are inconsistent or when the sample sizes of individual studies are small, as a meta-analysis can provide a more precise estimate of the true effect size.
When conducting a meta-analysis, researchers must carefully assess the risk of bias in each study to enhance the validity of the meta-analysis. Many aspects of research studies are prone to bias , such as the methodology and the reporting of results. Where studies exhibit a high risk of bias, authors may opt to exclude the study from the analysis or perform a subgroup or sensitivity analysis.
Advantages of a meta-analysis include:
- Combine the results of multiple studies, resulting in a larger sample size and increased statistical power, to provide a more comprehensive and precise estimate of the effect size of an intervention or outcome
- Can help to identify sources of heterogeneity or variability in the results of individual studies by exploring the influence of different study characteristics or subgroups
- Can help to resolve conflicting results or controversies in the literature by providing a more robust estimate of the effect size
Disadvantages of a meta-analysis include:
- Susceptible to publication bias , where studies with statistically significant or positive results are more likely to be published than studies with nonsignificant or negative results. This bias can lead to an overestimation of the treatment effect in a meta-analysis
- May not be appropriate if the studies included are too heterogeneous , as this can make it difficult to draw meaningful conclusions from the pooled results
- Depend on the quality and completeness of the data available from the individual studies and may be limited by the lack of data on certain outcomes or subgroups
An ecological study assesses the relationship between outcome and exposure at a population level or among groups of people rather than studying individuals directly.
The main goal of an ecological study is to observe and analyse patterns or trends at the population level and to identify potential associations or correlations between environmental factors or exposures and health outcomes.
Ecological studies focus on collecting data on population health outcomes , such as disease or mortality rates, and environmental factors or exposures, such as air pollution, temperature, or socioeconomic status.
Example of an ecological study
An ecological study might be used when comparing smoking rates and lung cancer incidence across different countries.
Advantages of an ecological study include:
- Provide insights into how social, economic, and environmental factors may impact health outcomes in real-world settings , which can inform public health policies and interventions
- Cost-effective and efficient, often using existing data or readily available data, such as data from national or regional databases
Disadvantages of an ecological study include:
- Ecological fallacy occurs when conclusions about individual-level associations are drawn from population-level differences
- Ecological studies rely on population-level (i.e. aggregate) rather than individual-level data; they cannot establish causal relationships between exposures and outcomes, as the studies do not account for differences or confounders at the individual level
Randomised controlled trial
A randomised controlled trial (RCT) is an important study design commonly used in medical research to determine the effectiveness of a treatment or intervention . It is considered the gold standard in research design because it allows researchers to draw cause-and-effect conclusions about the effects of an intervention.
In an RCT, participants are randomly assigned to two or more groups. One group receives the intervention being tested, such as a new drug or a specific medical procedure. In contrast, the other group is a control group and receives either no intervention or a placebo .
Randomisation ensures that each participant has an equal chance of being assigned to either group, thereby minimising selection bias . To reduce bias, an RCT often uses a technique called blinding , in which study participants, researchers, or analysts are kept unaware of participant assignment during the study. The participants are then followed over time, and outcome measures are collected and compared to determine if there is any statistical difference between the intervention and control groups.
Example of a randomised controlled trial
An RCT might be employed to evaluate the effectiveness of a new smoking cessation program in helping individuals quit smoking compared to the existing standard of care.
Advantages of an RCT include:
- Considered the most reliable study design for establishing causal relationships between interventions and outcomes and determining the effectiveness of interventions
- Randomisation of participants to intervention and control groups ensures that the groups are similar at the outset, reducing the risk of selection bias and enhancing internal validity
- Using a control group allows researchers to compare with the group that received the intervention while controlling for confounding factors
Disadvantages of an RCT include:
- Can raise ethical concerns ; for example, it may be considered unethical to withhold an intervention from a control group, especially if the intervention is known to be effective
- Can be expensive and time-consuming to conduct, requiring resources for participant recruitment, randomisation, data collection, and analysis
- Often have strict inclusion and exclusion criteria , which may limit the generalisability of the findings to broader populations
- May not always be feasible or practical for certain research questions, especially in rare diseases or when studying long-term outcomes
Dr Chris Jefferies
- Yuliya L, Qazi MA (eds.). Toronto Notes 2022. Toronto: Toronto Notes for Medical Students Inc; 2022.
- Le T, Bhushan V, Qui C, Chalise A, Kaparaliotis P, Coleman C, Kallianos K. First Aid for the USMLE Step 1 2023. New York: McGraw-Hill Education; 2023.
- Rothman KJ, Greenland S, Lash T. Modern Epidemiology. 3 rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.
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- What Is a Research Design | Types, Guide & Examples
What Is a Research Design | Types, Guide & Examples
Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:
- Your overall research objectives and approach
- Whether you’ll rely on primary research or secondary research
- Your sampling methods or criteria for selecting subjects
- Your data collection methods
- The procedures you’ll follow to collect data
- Your data analysis methods
A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.
Table of contents
Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.
Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.
There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.
The first choice you need to make is whether you’ll take a qualitative or quantitative approach.
Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.
Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.
It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.
Practical and ethical considerations when designing research
As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .
- How much time do you have to collect data and write up the research?
- Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
- Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
- Will you need ethical approval ?
At each stage of the research design process, make sure that your choices are practically feasible.
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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.
Types of quantitative research designs
Quantitative designs can be split into four main types.
- Experimental and quasi-experimental designs allow you to test cause-and-effect relationships
- Descriptive and correlational designs allow you to measure variables and describe relationships between them.
With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).
Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.
Types of qualitative research designs
Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.
The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.
Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.
In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.
Defining the population
A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.
For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?
The more precisely you define your population, the easier it will be to gather a representative sample.
- Sampling methods
Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.
To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.
Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.
For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.
Case selection in qualitative research
In some types of qualitative designs, sampling may not be relevant.
For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.
In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .
For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.
Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.
You can choose just one data collection method, or use several methods in the same study.
Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .
Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.
Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.
Other methods of data collection
There are many other ways you might collect data depending on your field and topic.
If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.
If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.
With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.
Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.
However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.
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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.
Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.
Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.
If you’re using observations , which events or actions will you count?
If you’re using surveys , which questions will you ask and what range of responses will be offered?
You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.
Reliability and validity
Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.
For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.
If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.
As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.
That means making decisions about things like:
- How many participants do you need for an adequate sample size?
- What inclusion and exclusion criteria will you use to identify eligible participants?
- How will you contact your sample—by mail, online, by phone, or in person?
If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?
If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?
It’s also important to create a data management plan for organizing and storing your data.
Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.
Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).
On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.
Quantitative data analysis
In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.
Using descriptive statistics , you can summarize your sample data in terms of:
- The distribution of the data (e.g., the frequency of each score on a test)
- The central tendency of the data (e.g., the mean to describe the average score)
- The variability of the data (e.g., the standard deviation to describe how spread out the scores are)
The specific calculations you can do depend on the level of measurement of your variables.
Using inferential statistics , you can:
- Make estimates about the population based on your sample data.
- Test hypotheses about a relationship between variables.
Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.
Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.
Qualitative data analysis
In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.
Two of the most common approaches to doing this are thematic analysis and discourse analysis .
There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.
If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.
- Simple random sampling
- Stratified sampling
- Cluster sampling
- Likert scales
- Null hypothesis
- Statistical power
- Probability distribution
- Effect size
- Poisson distribution
- Optimism bias
- Cognitive bias
- Implicit bias
- Hawthorne effect
- Anchoring bias
- Explicit bias
A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.
A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.
Quantitative research designs can be divided into two main categories:
- Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
- Experimental and quasi-experimental designs are used to test causal relationships .
Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.
The priorities of a research design can vary depending on the field, but you usually have to specify:
- Your research questions and/or hypotheses
- Your overall approach (e.g., qualitative or quantitative )
- The type of design you’re using (e.g., a survey , experiment , or case study )
- Your data collection methods (e.g., questionnaires , observations)
- Your data collection procedures (e.g., operationalization , timing and data management)
- Your data analysis methods (e.g., statistical tests or thematic analysis )
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
Operationalization means turning abstract conceptual ideas into measurable observations.
For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.
Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.
A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.
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Research Design 101
Everything You Need To Get Started (With Examples)
By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023
Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.
Overview: Research Design 101
What is research design.
- Research design types for quantitative studies
- Video explainer : quantitative research design
- Research design types for qualitative studies
- Video explainer : qualitative research design
- How to choose a research design
- Key takeaways
Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.
Understanding different types of research designs is essential as helps ensure that your approach is suitable given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.
The problem with defining research design…
One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.
Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!
In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.
Research Design: Quantitative Studies
Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental .
Descriptive Research Design
As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.
For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.
The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.
Correlational Research Design
Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.
For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).
As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).
That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…
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Experimental Research Design
Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.
For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.
Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.
Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.
Quasi-Experimental Research Design
Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.
For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.
Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.
All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .
Research Design: Qualitative Studies
There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.
Phenomenological Research Design
Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.
For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.
Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.
Grounded Theory Research Design
Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.
As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).
Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .
As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.
Ethnographic Research Design
Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .
All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.
As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.
As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.
Case Study Design
With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .
As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.
While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.
As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.
How To Choose A Research Design
Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.
Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!
Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.
Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.
Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.
Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.
Recap: Key Takeaways
We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:
- Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
- Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
- Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
- When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.
If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .
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Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.
Thanks this was quite valuable to clarify such an important concept.
Thanks for this simplified explanations. it is quite very helpful.
This was really helpful. thanks
Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.
Please is there any template for a case study research design whose data type is a secondary data on your repository?
This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.
This post is helpful, easy to understand, and deconstructs what a research design is. Thanks
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What is research design? Understand types of research design, with examples
Have you been wondering “ what is research design ?” or “what are some research design examples ?” Are you unsure about the research design elements or which of the different types of research design best suit your study? Don’t worry! In this article, we’ve got you covered!
Table of Contents
What is research design?
Have you been wondering “ what is research design ?” or “what are some research design examples ?” Don’t worry! In this article, we’ve got you covered!
A research design is the plan or framework used to conduct a research study. It involves outlining the overall approach and methods that will be used to collect and analyze data in order to answer research questions or test hypotheses. A well-designed research study should have a clear and well-defined research question, a detailed plan for collecting data, and a method for analyzing and interpreting the results. A well-thought-out research design addresses all these features.
Research design elements
Research design elements include the following:
- Clear purpose: The research question or hypothesis must be clearly defined and focused.
- Sampling: This includes decisions about sample size, sampling method, and criteria for inclusion or exclusion. The approach varies for different research design types .
- Data collection: This research design element involves the process of gathering data or information from the study participants or sources. It includes decisions about what data to collect, how to collect it, and the tools or instruments that will be used.
- Data analysis: All research design types require analysis and interpretation of the data collected. This research design element includes decisions about the statistical tests or methods that will be used to analyze the data, as well as any potential confounding variables or biases that may need to be addressed.
- Type of research methodology: This includes decisions about the overall approach for the study.
- Time frame: An important research design element is the time frame, which includes decisions about the duration of the study, the timeline for data collection and analysis, and follow-up periods.
- Ethical considerations: The research design must include decisions about ethical considerations such as informed consent, confidentiality, and participant protection.
- Resources: A good research design takes into account decisions about the budget, staffing, and other resources needed to carry out the study.
The elements of research design should be carefully planned and executed to ensure the validity and reliability of the study findings. Let’s go deeper into the concepts of research design .
Characteristics of research design
Some basic characteristics of research design are common to different research design types . These characteristics of research design are as follows:
- Neutrality : Right from the study assumptions to setting up the study, a neutral stance must be maintained, free of pre-conceived notions. The researcher’s expectations or beliefs should not color the findings or interpretation of the findings. Accordingly, a good research design should address potential sources of bias and confounding factors to be able to yield unbiased and neutral results.
- Reliability : Reliability is one of the characteristics of research design that refers to consistency in measurement over repeated measures and fewer random errors. A reliable research design must allow for results to be consistent, with few errors due to chance.
- Validity : Validity refers to the minimization of nonrandom (systematic) errors. A good research design must employ measurement tools that ensure validity of the results.
- Generalizability: The outcome of the research design should be applicable to a larger population and not just a small sample . A generalized method means the study can be conducted on any part of a population with similar accuracy.
- Flexibility: A research design should allow for changes to be made to the research plan as needed, based on the data collected and the outcomes of the study
A well-planned research design is critical for conducting a scientifically rigorous study that will generate neutral, reliable, valid, and generalizable results. At the same time, it should allow some level of flexibility.
Different types of research design
A research design is essential to systematically investigate, understand, and interpret phenomena of interest. Let’s look at different types of research design and research design examples .
Broadly, research design types can be divided into qualitative and quantitative research.
Qualitative research is subjective and exploratory. It determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc.
Quantitative research is objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research is usually done using surveys and experiments.
Qualitative research vs. Quantitative research
Qualitative research design types and qualitative research design examples .
The following will familiarize you with the research design categories in qualitative research:
- Grounded theory: This design is used to investigate research questions that have not previously been studied in depth. Also referred to as exploratory design , it creates sequential guidelines, offers strategies for inquiry, and makes data collection and analysis more efficient in qualitative research.
Example: A researcher wants to study how people adopt a certain app. The researcher collects data through interviews and then analyzes the data to look for patterns. These patterns are used to develop a theory about how people adopt that app.
- Thematic analysis: This design is used to compare the data collected in past research to find similar themes in qualitative research.
Example: A researcher examines an interview transcript to identify common themes, say, topics or patterns emerging repeatedly.
- Discourse analysis : This research design deals with language or social contexts used in data gathering in qualitative research.
Example: Identifying ideological frameworks and viewpoints of writers of a series of policies.
Quantitative research design types and quantitative research design examples
Note the following research design categories in quantitative research:
- Descriptive research design : This quantitative research design is applied where the aim is to identify characteristics, frequencies, trends, and categories. It may not often begin with a hypothesis. The basis of this research type is a description of an identified variable. This research design type describes the “what,” “when,” “where,” or “how” of phenomena (but not the “why”).
Example: A study on the different income levels of people who use nutritional supplements regularly.
- Correlational research design : Correlation reflects the strength and/or direction of the relationship among variables. The direction of a correlation can be positive or negative. Correlational research design helps researchers establish a relationship between two variables without the researcher controlling any of them.
Example : An example of correlational research design could be studying the correlation between time spent watching crime shows and aggressive behavior in teenagers.
- Diagnostic research design : In diagnostic design, the researcher aims to understand the underlying cause of a specific topic or phenomenon (usually an area of improvement) and find the most effective solution. In simpler terms, a researcher seeks an accurate “diagnosis” of a problem and identifies a solution.
Example : A researcher analyzing customer feedback and reviews to identify areas where an app can be improved.
- Explanatory research design : In explanatory research design , a researcher uses their ideas and thoughts on a topic to explore their theories in more depth. This design is used to explore a phenomenon when limited information is available. It can help increase current understanding of unexplored aspects of a subject. It is thus a kind of “starting point” for future research.
Example : Formulating hypotheses to guide future studies on delaying school start times for better mental health in teenagers.
- Causal research design : This can be considered a type of explanatory research. Causal research design seeks to define a cause and effect in its data. The researcher does not use a randomly chosen control group but naturally or pre-existing groupings. Importantly, the researcher does not manipulate the independent variable.
Example : Comparing school dropout levels and possible bullying events.
- Experimental research design : This research design is used to study causal relationships . One or more independent variables are manipulated, and their effect on one or more dependent variables is measured.
Example: Determining the efficacy of a new vaccine plan for influenza.
Benefits of research design
T here are numerous benefits of research design . These are as follows:
- Clear direction: Among the benefits of research design , the main one is providing direction to the research and guiding the choice of clear objectives, which help the researcher to focus on the specific research questions or hypotheses they want to investigate.
- Control: Through a proper research design , researchers can control variables, identify potential confounding factors, and use randomization to minimize bias and increase the reliability of their findings.
- Replication: Research designs provide the opportunity for replication. This helps to confirm the findings of a study and ensures that the results are not due to chance or other factors. Thus, a well-chosen research design also eliminates bias and errors.
- Validity: A research design ensures the validity of the research, i.e., whether the results truly reflect the phenomenon being investigated.
- Reliability: Benefits of research design also include reducing inaccuracies and ensuring the reliability of the research (i.e., consistency of the research results over time, across different samples, and under different conditions).
- Efficiency: A strong research design helps increase the efficiency of the research process. Researchers can use a variety of designs to investigate their research questions, choose the most appropriate research design for their study, and use statistical analysis to make the most of their data. By effectively describing the data necessary for an adequate test of the hypotheses and explaining how such data will be obtained, research design saves a researcher’s time.
Overall, an appropriately chosen and executed research design helps researchers to conduct high-quality research, draw meaningful conclusions, and contribute to the advancement of knowledge in their field.
Frequently Asked Questions (FAQ) on Research Design
Q: What are th e main types of research design?
Broadly speaking there are two basic types of research design –
qualitative and quantitative research. Qualitative research is subjective and exploratory; it determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc. Quantitative research , on the other hand, is more objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research design is usually done using surveys and experiments.
Q: How do I choose the appropriate research design for my study?
Choosing the appropriate research design for your study requires careful consideration of various factors. Start by clarifying your research objectives and the type of data you need to collect. Determine whether your study is exploratory, descriptive, or experimental in nature. Consider the availability of resources, time constraints, and the feasibility of implementing the different research designs. Review existing literature to identify similar studies and their research designs, which can serve as a guide. Ultimately, the chosen research design should align with your research questions, provide the necessary data to answer them, and be feasible given your own specific requirements/constraints.
Q: Can research design be modified during the course of a study?
Yes, research design can be modified during the course of a study based on emerging insights, practical constraints, or unforeseen circumstances. Research is an iterative process and, as new data is collected and analyzed, it may become necessary to adjust or refine the research design. However, any modifications should be made judiciously and with careful consideration of their impact on the study’s integrity and validity. It is advisable to document any changes made to the research design, along with a clear rationale for the modifications, in order to maintain transparency and allow for proper interpretation of the results.
Q: How can I ensure the validity and reliability of my research design?
Validity refers to the accuracy and meaningfulness of your study’s findings, while reliability relates to the consistency and stability of the measurements or observations. To enhance validity, carefully define your research variables, use established measurement scales or protocols, and collect data through appropriate methods. Consider conducting a pilot study to identify and address any potential issues before full implementation. To enhance reliability, use standardized procedures, conduct inter-rater or test-retest reliability checks, and employ appropriate statistical techniques for data analysis. It is also essential to document and report your methodology clearly, allowing for replication and scrutiny by other researchers.
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